Large broad-leaved canopy of banana (Musa nana Lour.) induces dramatically high spatial–temporal variability of throughfall

Throughfall (TF) is an important water input of rainfall redistribution into floor, and its spatial–temporal variability under some species' canopies has been documented to evaluate effect on splash erosion. However, the understanding of TF variability under large broad-leaved canopy remains insufficient. In this study, the spatial heterogeneity, temporal stability and drop size of TF were quantified using variogram fitting, normalised ranking and filter paper staining, respectively, under banana (Musa nana Lour.) canopy comprising long and wide leaves. Results indicated TF pattern showed strong spatial correlation at a range of 3–5 m. High spatial variability of TF was found, which was affected by rainfall event size and was accompanied by great canopy disturbance. TF plots revealed high time variability, which was mainly controlled by unstable banana canopy structure. TF drop size from leaf dripping points varied in 3–10 mm and showed significant differences (p < 0.05) among five kinds of leaf shapes, implying that concave and broken banana leaves were involved in the variability of TF drop size. Overall, results demonstrate the spatial–temporal variability of TF is dramatically induced by banana canopy with broad leaves, which may result in non-uniform soil water content and splash erosion under the canopy.

Mapping long-period soil resonances in the Kathmandu basin using microtremors

This study reports the geostatistical analysis of a set of 40 single-station horizontal-to-vertical spectral ratio (HVSR) passive seismic survey data collected in the Kathmandu basin (Nepal). The Kathmandu basin is characterized by a heterogeneous sedimentary cover and by a complex geo-structural setting, inducing a high spatial variability of the bedrock depth. Due to the complex geological setting, the interpretation and analysis of soil resonance periods derived from the HVSR surveys is challenging, both from the perspective of bedrock depth estimation as well as of seismic-site effects characterization. To exploit the available information, the HVSR data are analyzed by means of a geostatistical approach. First, the spatial continuity structure of HVSR data is investigated and interpreted taking into consideration the geological setting and available stratigraphic and seismic information. Then, the exploitation of potential auxiliary variables, based on surface morphology and distance from outcropping bedrock, is evaluated. Finally, the mapping of HVSR resonance periods, together with the evaluation of interpolation uncertainty, is obtained by means of kriging with external drift interpolation. This work contributes to the characterization of local seismic response of the Kathmandu basin. The resulting map of soil resonance periods is compatible with the results of preceding studies and it is characterized by a high spatial variability, even in areas with a deep bedrock and long resonance periods.

Hydroclimatic variability in Tomi at Sibut, Gribingui at Kaga-Bandoro and Fafa at Bouca basins, in the Central African Republic

The research of ruptures on rainfall and discharge serial data from 1950 to 1995 of three small catchments (from 2000 to 6000 km2) of the Central African Republic, at the boundary between Chad and Congo basins, has shown a high spatial variability. The rupture observed in 1970 at the subcontinental scale, which started the drought period in West and Central Africa, is observed only on the Northward basin, the driest. Then it was difficult to compare the hydroclimatic periods from a basin to another one. However, all the studied basins have shown a degradation of the hydrological regime from the end of the 1980s onward, with a severe level since the end of 1980s. The depletion coefficients have the same range for the 3 studied basins than for the Ubangi River basin, widening the drought impact.

Clustering CAMELS using hydrological signatures with high spatial predictability

The behavior of every catchment is unique. Still, we need ways to classify them as this helps to improve hydrological theories. Usually catchments are classified along either their attributes classes (e.g. climate, topography) or their discharge characteristics, which is often captured in hydrological signatures. However, recent studies have shown that many hydrological signatures have a low predictability in space and therefore only dubious hydrological meaning. Therefore, this study uses hydrological signatures with the highest predictability in space to cluster 643 catchments from the continental United States (CAMELS (Catchment Attributes and MEteorology for Large-Sample Studies) dataset) into ten groups. We then evaluated the connection between catchment attributes with the hydrological signatures with quadratic regression, both in the overall CAMELS dataset and the ten clusters. In the overall dataset, aridity had the strongest connection to the hydrological signatures, especially in the eastern United States. However, the clusters in the western United States showed a more heterogeneous pattern with a larger influence of forest fraction, the mean elevation or the snow fraction. From this, we conclude that catchment behavior can be mainly attributed to climate in regions with homogenous topography. In regions with a heterogeneous topography, there is no clear pattern of the catchment behavior, as catchments show high spatial variability in their attributes. The classification of the CAMELS dataset with the hydrological signatures allows testing hydrological models in contrasting environments.

Large spatial variations in the flux balance along the front of a Greenland tidewater glacier

The frontal flux balance of a medium-sized tidewater glacier in western Greenland in the summer is assessed by quantifying the individual components (ice flux, retreat, calving, and submarine melting) through a combination of data and models. Ice flux and retreat are obtained from satellite data. Submarine melting is derived using a high-resolution ocean model informed by near-ice observations, and calving is estimated using a record of calving events along the ice front. All terms exhibit large spatial variability along the ∼5 km wide ice front. It is found that submarine melting accounts for much of the frontal ablation in small regions where two subglacial discharge plumes emerge at the ice front. Away from the subglacial plumes, the estimated melting accounts for a small fraction of frontal ablation. Glacier-wide, these estimates suggest that mass loss is largely controlled by calving. This result, however, is at odds with the limited presence of icebergs at this calving front – suggesting that melt rates in regions outside of the subglacial plumes may be underestimated. Finally, we argue that localized melt incisions into the glacier front can be significant drivers of calving. Our results suggest a complex interplay of melting and calving marked by high spatial variability along the glacier front.

Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model

For estimation of maximum daily fresh snow accumulation (MDFSA), a novel model based on an artificial neural network (ANN) was proposed. Daily precipitation, mean temperature, and minimum temperature were used as the input data for the ANN model. The ANN model was regularized and trained using a set of 19,923 data points, observed daily in South Korea between 1960 and 2016. Leave-one-out cross validation was performed to validate the model. When the input data were known at the gauged locations, the correlation coefficient between the observed MDFSA and the estimated one by the ANN model was 0.90. When the input data were spatially interpolated at ungauged locations using the ordinary kriging (OK) method, the correlation coefficient was 0.40. The difference in correlation coefficients between the two methods implies that, while the ANN model itself has good performance, a significant portion of the uncertainty of the estimated MDFSA at ungauged locations comes from high spatial variability of the input variables that cannot be captured by the network of in situ gauges. However, these correlation coefficients were significantly greater than the correlation coefficient obtained by spatially interpolating the MDFSA values with the OK method (R = 0.20). These findings suggest that our ANN model significantly reduces the uncertainty of the estimated MDFSA caused by its high spatial variability.